Data Driven Systems Biology: Harnessing Big Data and Systems Approaches to Decode Complex Biology

Title: TBA

Bio: Prof. Alfonso Valencia is ICREA research Professor, Director of the Life Sciences Department of the Barcelona Supercomputing Center, Director of the Spanish National Bioinformatics Institute INB/ELIXIR-ES and coordinator of the data pillar of the Spanish Personalised Medicine intiative, IMPaCT. His research interest is the development of Computational Biology methods and their application to biomedical problems. Some of the computational methods he developed are considered pioneering work in areas such as biological text mining, protein coevolution, disease networks and more recently modelling cellular systems (digital twins). He participates in some of the key cancer related international consortia. In terms of community services, he is one of the initial promoters of the ELIXIR infrastructure, founder of the Spanish and International Bioinformatics networks and former president of ISCB, the international professional association of Bioinformaticians. He is Executive Editor of the main journal in the field (Bioinformatics OUP).

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Title: From Omics to Mechanisms: Deep Learning Models of Molecular Networks for Precision Cancer Medicine

Bio: Avlant Nilsson is an Assistant Professor in Precision Medicine at the Department of Cell and Molecular Biology, Karolinska Institutet, and a group leader at SciLifeLab through the DDLS program. He holds an MSc (2014) and a PhD (2019) in Biological Engineering from Chalmers University of Technology, where his thesis focused on the metabolism of proliferating cells, including liver cancer. He then pursued postdoctoral research at the Massachusetts Institute of Technology (2019–2023), developing neural network models of signal transduction in immune cells. His research group, currently comprising of two PhD students and two postdoctoral researchers, develops data-driven models of molecular networks to understand how genetic alterations, cell type of origin, and cell–cell interactions shape cancer biology. The long-term goal of the lab is to advance computer-aided design of cancer medicine by predicting drug responses, resistance mechanisms, and microenvironmental interactions.

Abstract: TBA

Title: Spatially resolving B cell clonal dynamics in cancer and beyond

Bio: Dr. Camilla Engblom is a SciLifeLab Fellow and an Assistant Professor in the Division of Immunology and Respiratory Medicine and the Department of Medicine, Solna at the Karolinska Institutet (KI). Dr. Engblom received her PhD in Immunology from Harvard University in 2017 focusing on long-range cancer-host interactions involving myeloid cells (Dr. Mikael Pittet’s lab at Massachusetts General Hospital/Harvard Medical School). As a MSCA postdoctoral fellow in Dr. Jonas Frisén’s lab (KI), Dr. Engblom developed a spatial transcriptomics-based tool (Spatial VDJ) to map B cell and T cell receptors within human tissues. Located at SciLifeLab and the Center for Molecular Medicine (KI), the Engblom lab’s main research focus is to spatially and functionally resolve B cell clonal dynamics in cancer tissues and beyond.

Abstract: B cells perform functions critical to human health, including antibody production and antigen presentation. B cells develop, differentiate, and expand in spatially distinct sites across the body. B cells express clonal heritable B cell receptors (BCR) that confer exquisite molecular (i.e., antigen) specificity. B cell receptors can be defined by sequencing. Linking specific BCR sequences to their molecular and cellular surroundings, i.e., ‘clonal niche’, could help us understand and harness B cell activity. A technological bottleneck has been to capture the location of BCR sequences, and by extension B cell clonal responses, directly within tissues. We recently developed a spatial transcriptomics-based approach (Spatial VDJ) and associated computational pipelines to reconstruct B cell clonality in human tissues. Here, we present adaptation of Spatial VDJ to murine tissue to enable preclinical studies and B cell receptor dynamics under inflammatory conditions, including cancer.

Title: Enabling technologies for spatial metabolomics: Moving from single cells to 3D-space exploration in mixed reality

Abstract: MALDI-Mass spectrometry imaging (MSI), also referred to as spatial metabolomics, has emerged as a powerful technology for spatially resolved analysis and visualization of lipids and metabolites in systems biology and clinical research. Advancement of MSI requires rapid progress in multiple areas such as instrumentation, experimental workflows and computational strategies to harness big data. The talk will therefore initially review a “classic” technology show case using tissue slices: Spatial metabolomics revealed that Tet3 knockout enterocytes exhibit an unphysiological metabolic profile when compared with their wild-type counterparts suggesting that terminal cell differentiation is regulated by TET3 at the metabolic level. MSI technology has recently moved into two new directions: Single-cell metabolomics and 3D-reconstructed metabolomics.

To study proinflammatory activation of iPSC-derived microglia by bacterial lipopolysaccharide (LPS), we developed the PRISM-MS (PRescan Imaging for Small Molecule – Mass Spectrometry) platform for analysis and on-cell MS2 identification of low mass metabolites (<200 Da) in large cell populations. Itaconate and taurine were identified as markers for “activated” versus “resting” microglia, respectively. Translation of single cell results to endogenous microglia in organotypic rat hippocampal slice cultures indicated that LPS-activation involves changes of the itaconate-to-taurine ratio and alterations in neuron-to-glia glutamine-glutamate shuttling.

To investigate fibroblast-colon cancer cell interactions in a simple 3D-culture model and in patient-derived organoids (PDOs), we built a translational 3D MSI platform as an end-to-end solution for 3D-enabling sample preparation, 3D-reconstruction and data processing, 3D-rendering, and immersive user interaction with organoid big data in a mixed reality. When applied to colon cancer PDOs, the methodology revealed that fluid-filled cysts characteristic of these PDOs were rich in purine nucleotides.

Title: Harnessing Big Data for Network Biology with FunCoup 6

Bio: Erik Sonnhammer is Professor of Bioinformatics at Stockholm University, and previously had the same position at Karolinska Institutet, Stockholm. He did a Ph.D. in bioinformatics at the Sanger Institute in Cambridge, England. His research interests are in network and systems biology to understand gene and protein function on a large scale. The group has made many contributions to Gene Regulatory Network analysis, including inference, benchmarking, and simulation.

Abstract: TBA

Title: Learning cellular dynamics of tissues from single-cell and spatial omics

Bio: Jean researches mathematical rules in the molecular tricks that cancer cells use to escape destruction by immune cells. We seek to articulate the molecular chat between immune and cancer cells into equations, to serve as the foundation to engineer personalized cancer immunotherapy. We combine single-cell and spatial tumor profiling experiments, machine-learning & data science, and physics-style mathematical modeling.

Abstract: TBA

Title: Extracting protein-protein interactions from the literature with deep learning-based text mining

Bio: Katerina Nastou holds a Ph.D. in Bioinformatics and is a researcher at Statens Serum Institut in Copenhagen, specializing in multi-omics data analysis, biomedical text mining, and systems biology. Her work focuses on applying deep learning to extract and model molecular relationships from large-scale biological data and the scientific literature. She has contributed to the STRING database, a leading resource on protein networks, by upgrading its text-mining channel with advanced deep learning-based language models. She also currently collaborates internationally on projects such as AIM-HEART and EPOCH.

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Title: That’s Gonna Leave a Mark: Computational inference of complex cell features

Bio: Marcel studied Biology and Bioinformatics in Germany before starting his PhD in Computational Biology at Stockholm University. In the lab of Marc Friedländer he characterized subtle gene expression variations in virtually identical cells – linking them to regulatory layers and showing their predictive potential. He moved to the lab of Vicent Pelechano at Karolinska Institute for his postdoc to investigate single-cell RNA degradation dynamics and cell lineage relationships – resulting in pioneering work which showed that cellular ancestries can be predicted using only gene expression. In 2025, he started his lab as a DDLS fellow in precision medicine and diagnostics at Uppsala University and SciLifeLab, focusing on computational approaches to infer complex cell features, such as lineage and micro-environment, to characterize cancer heterogeneity and phenotype switches.

Abstract: TBA

Title: “Integrating protein interaction maps and omics for explainable health indicators”

Bio: Mika Gustafsson is a Professor in Translational Bioinformatics (PhD in Theoretical Physics, 2010) at the Department of Physics, Chemistry and Biology, Technical Faculty, Linköping University. Over the past ten years, he has led a research group of five to seven members. His core expertise lies in creating and integrating network analyses with omics and has been developing machine learning methods for precision medicine. In many projects, he has led medical doctors and molecular biologists in testing and validating omics-based findings, working primarily on complex diseases such as multiple sclerosis.

Abstract: TBA

Title: Dynamics of immunological tissue architecture linking inflammation with colorectal cancer

Bio: Simon Koplev is a SciLifeLab Fellow and newly appointed group leader in computational biology at KTH Royal Institute of Technology, Department of Gene Technology. He leads a computational biology research group investigating the fundamental principles and architecture of human tissues across organs in healthy steady-state and disease perturbations. The group is engaged with collaborative large-scale and open science efforts such as the Human Cell Atlas, developing the next generation of reference datasets and computational methods. Simon holds a PhD in Medical Science from the University of Cambridge at the Cancer Research UK Cambridge Institute supervised by John Marioni and Martin Miller. He did his postdoc with Sarah Teichmann at the Sanger Institute and Cambridge Stem Cell Institute, working on human single-cell and spatial studies of intestinal fibroblasts. Simon has 12 years of experience in bioinformatics research having published with more than 500 co-authors 35 peer-reviewed papers, spanning research on cancer, cardiovascular diseases, fibroblasts, gene regulatory networks, and computational methods development using machine learning. He holds a MScEng in Systems Biology from the Technical University of Denmark, supervised by Søren Brunak, including 2 semesters as a Research Scholar at the Dana-Farber Cancer Institute, Harvard Medical School. Simon began his scientific career with a BS in Biochemistry from the University of Copenhagen.

Abstract: TBA